ABSTRACT. Much of the work in political science would greatly benefit if we would be able to
make predictions about the future, this is especially true for the field of international relations.
Yet, making sensible forecasts about the future is extremely hard. In this paper we propose
the use of Ensemble Bayesian Model Averaging (EBMA) modified to be applicable to binary
dependent variables. This process combines different statistical component models to increase
the accuracy of out-of-sample forecasts. By using EBMA we combine the strengths of different
component models to generate predictions with higher accuracy. After explaining our modified
approach to EBMA we test the superiority of this process to the individual model out-of-sample
forecasts on monthly data on insurgency in 29 Asian countries. We show that compared to the
individual component models, EBMA increases the accuracy of the out-of-sample forecast on
almost all metrics.

ABSTRACT. Much of the work in political science would greatly benefit if we would be able to
make predictions about the future, this is especially true for the field of international relations.
Yet, making sensible forecasts about the future is extremely hard. In this paper we propose
the use of Ensemble Bayesian Model Averaging (EBMA) modified to be applicable to binary
dependent variables. This process combines different statistical component models to increase
the accuracy of out-of-sample forecasts. By using EBMA we combine the strengths of different
component models to generate predictions with higher accuracy. After explaining our modified
approach to EBMA we test the superiority of this process to the individual model out-of-sample
forecasts on monthly data on insurgency in 29 Asian countries. We show that compared to the
individual component models, EBMA increases the accuracy of the out-of-sample forecast on
almost all metrics.